1. Field of the Invention
This invention relates to a system for detecting mines and, more particularly, to a system for automatically discriminating a side-scan sonar image to determine whether or not there is a mine in the image.
2. Description of Prior Art
Mine detection typically has been performed by analysts viewing side scan sonar images in which the mine appears as a highlight area followed by a shadow area in the range direction. The difficulty of this detection task varies greatly depending on the nature of the background image. On silted areas, only a highlight appears since the background is as dark as the shadow. On the sand, the mine images as a highlight, usually followed by a well-defined shadow. In rocky areas, mines and rocks are often indistinguishable from one another. The way in which the mine lies on the ocean floor relative to the sonar platform affects the intensity and shape of the highlight. At near range, shadows may not be apparent, while at far range they can be quite long.
The sonar analyst must, therefore, bring a great deal of experience to the task of identifying mines using sonar images. This task becomes harder when large amounts of data must be reviewed in a short time. As the data rate increases the analyst's performance drops; that is, more objects are incorrectly classified. Moreover, analysts tend to fatigue with time on the job; a fresh analyst is correct more often than a tired analyst who has been looking at data for several hours. Accordingly, there is a need for an automatic system to aid the analyst in detecting mines.
Although automatic detection systems are not troubled by speed or fatigue, it is difficult to design a machine to match the visual pattern recognition talent of a human analyst. Machine mine detection systems have used a variety of means to detect mines in sonar images including matched filters, neural networks and statistically based cuers. The more successful approaches use a two stage process of detection of areas of interest followed by discrimination based on a more intensive look at the areas of interest. The discrimination phase extracts features based on the shape, texture and intensity of the highlight or shadow and feeds these to a Bayesian based classifier. General classifiers designed to discriminate between man-made objects and natural objects are not specifically optimized to the image features of mines and hence are suboptimal in their performance of mine detection. Accordingly there is a need for a discriminator specifically developed for mine detection.